摘要
雷达高分辨距离像(HRRP)识别,是军事目标识别的一个重要手段。支持向量机(SVM)具有良好的泛化能力,适用于小样本学习问题。本文针对3类飞机目标的HRRP数据,构造了SVM分类器,设计了2组实验以比较SVM与最大相关系数法(MCM)的泛化能力、识别速度和抗噪能力。实验结果表明,SVM在军事目标HRRP分类方面具有良好的应用前景。
Radar high range resolution profile (HRRP) classification is an important method of military target recognition. Because of its good generalization property, support vector machine (SVM) has better performance on small training set learning. Based on the HRRPs of 3 types of aircraft, 2 experiments are designed to compare the performance of SVM method and maximum correlation method (MCM) on generalization property, classification speed and noise resistance. It is demonstrated by experimental results that SVM...
出处
《微计算机信息》
北大核心
2008年第10期254-255,共2页
Control & Automation
关键词
高分辨距离像
雷达目标分类
支持向量机
High range resolution profile
Radar target classification
Support vector machine.
